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All Journal Bulletin of Electrical Engineering and Informatics Journal of Applied Data Sciences
Alrawashdeh, Tawfiq
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Smart indoor gardening: elevating growth, health, and automation Alrawashdeh, Tawfiq; Alkore Alshalabi, Ibrahim; Al-Jaafreh, Moha'med; Alksasbeh, Malek
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7101

Abstract

Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.
Detection of COVID-19 using EfficientnetV2-XL and Radam Optimizer from Chest X-ray Images Alshalabi, Ibrahim Alkore; Alrawashdeh, Tawfiq; Abusaleh, Sumaya; Alksasbeh, Malek Zakarya; Alemerien, Khalid; Al-Eidi, Shorouq; Alshamaseen, Hamzah
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.512

Abstract

Automating the detection of the COVID-19 pandemic has become necessary for assisting radiologists and medical practitioners in the diagnosis process. It enables them not only to save time through early diagnosis but also to ensure that they are making more accurate diagnoses. Therefore, this research presents a novel approach for automatically identifying COVID-19 in chest X-ray images by utilizing the EfficientNetV2-XL model in combination with the Rectified Adam optimizer for training. For conducting the experiments, we used the dataset available on Kaggle, known as the “COVID-19 Radiography Dataset.” The totality of this dataset was 21,165, and it included four patterns: COVID-19, viral pneumonia, lung opacity, and normal cases. The dataset was divided into 80% training and 20% testing. The preprocessing stage included resizing images to 512 × 512 pixels and then applying data augmentation techniques to enhance model robustness. Consequently, a fine-tuned multiclass categorization system was implemented. The proposed system's effectiveness is evidenced by the experimental outcomes, which show a 99.31% accuracy rate and a perfect Area Under the Curve score of 1 for identifying COVID-19. Additionally, the Score-CAM visualization method was utilized to enhance the interpretability of model predictions, identifying key regions within the chest X-ray images that influence the classification outcome. This Localization technique aids healthcare professionals in understanding the reasoning behind the model and confirming the accuracy of the diagnosis. The proposed system outperformed the state-of-the-art models for COVID-19 detection. 
Co-Authors Abusaleh, Sumaya Al-Eidi, Shorouq Al-Jaafreh, Moha'med Alemerien, Khalid Alkore Alshalabi, Ibrahim Alksasbeh, Malek Alksasbeh, Malek Zakarya Alshalabi, Ibrahim Alkore Alshamaseen, Hamzah